Studying at the University of Verona

Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.

Academic calendar

The academic calendar shows the deadlines and scheduled events that are relevant to students, teaching and technical-administrative staff of the University. Public holidays and University closures are also indicated. The academic year normally begins on 1 October each year and ends on 30 September of the following year.

Academic calendar

Course calendar

The Academic Calendar sets out the degree programme lecture and exam timetables, as well as the relevant university closure dates..

For the year 2015/2016 No calendar yet available

Exam calendar

Exam dates and rounds are managed by the relevant Science and Engineering Teaching and Student Services Unit.
To view all the exam sessions available, please use the Exam dashboard on ESSE3.
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Exam calendar

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Academic staff

A B C D F G L M P S

Accordini Simone

simone.accordini@univr.it +39 045 8027657

Belussi Alberto

alberto.belussi@univr.it +39 045 802 7980

Bicego Manuele

manuele.bicego@univr.it +39 045 802 7072

Bombieri Cristina

cristina.bombieri@univr.it 045-8027284

Bombieri Nicola

nicola.bombieri@univr.it +39 045 802 7094

Cicalese Ferdinando

ferdinando.cicalese@univr.it +39 045 802 7969

Combi Carlo

carlo.combi@univr.it 045 802 7985

Constantin Gabriela

gabriela.constantin@univr.it 045-8027102

Cristani Marco

marco.cristani@univr.it +39 045 802 7841

Daducci Alessandro

alessandro.daducci@univr.it +39 045 8027025

Delledonne Massimo

massimo.delledonne@univr.it 045 802 7962; Lab: 045 802 7058

Franco Giuditta

giuditta.franco@univr.it +39 045 802 7045

Giugno Rosalba

rosalba.giugno@univr.it 0458027066

Laudanna Carlo

carlo.laudanna@univr.it 045-8027689

Liptak Zsuzsanna

zsuzsanna.liptak@univr.it +39 045 802 7032

Malerba Giovanni

giovanni.malerba@univr.it 045/8027685

Manca Vincenzo

vincenzo.manca@univr.it 045 802 7981

Marcon Alessandro

alessandro.marcon@univr.it +39 045 802 7668

Menegaz Gloria

gloria.menegaz@univr.it +39 045 802 7024

Perduca Massimiliano

massimiliano.perduca@univr.it +39 045 802 7984

Sala Pietro

pietro.sala@univr.it 0458027850

Salvagno Gian Luca

gianluca.salvagno@univr.it 045 8124308-0456449264

Study Plan

The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University. Please select your Study Plan based on your enrolment year.

Training offer to be defined

Legend | Type of training activity (TTA)

TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.




SPlacements in companies, public or private institutions and professional associations

Teaching code

4S004553

Teacher

Pietro Sala

Coordinatore

Pietro Sala

Credits

6

Language

English en

Scientific Disciplinary Sector (SSD)

INF/01 - INFORMATICS

Period

II sem. dal Mar 1, 2017 al Jun 9, 2017.

Learning outcomes

Knowledge and understanding

The course aims to introduce principles that form the foundations of the Decision Support System with particular focus on their use in Biomedical applications.

Applying knowledge and understanding

During the course students will aquire the following competences:
- they will be able to choose and use the appropriate components in order to provide solution for supporting decision to the medical staff;
- they will be able torealize complex operations of Extraction, Transformation, and Loading (ETL) on several clinical data types coming from different sources (Relational Databases, API, Websites, and so on)
and encoded in both structure (relational tables)
and semi-structured (XML) fashion;
-they will be able to model and realize OLAP (On-Line Analytical Processing) solutions for supportuing decisions in a Biomedical context;
-they will be able to use or adapt advanced data-mining techniques (Approximate Functional Dependencies, Association Rules, Entropy-based Classifiers, and so on) for extracting knowledge from large amounts of data.

Making judgements

Allo studente verranno fornite le conoscenze necessarie per gestire in modo autonomo:
- la scelta e applicazione delle tecniche di data mining per l'estrazione di sapere medico da grosse quantità di dati;
- la scelta delle rappresentazioni grafiche e interattive più appropriate per la visualizzazione di determinate informazioni in ambito clinico.

Students will develop the required skills in order to be autonomous in the following tasks:

- choose and apply data mining techniques for extracting medical knowledge from large amount of data;
- choose the appropriate graphical/interactive representations for represent specific clinical information.

Communication skills

The students will be able to effectively use use BPMN as a medium for representing clinical processes as well as
ETL processes.

Learning skills

The students will be introduced to the main algorithms and techniques used in the clinical data mining field,
together with the description of the factors that affect their efficiency and effectiveness.
This knowledge will be the basis for comprehend more specific techniques adopted nowadays
for data mining for clinical domain.


Program

Functional Dependencies (FD):
concepts and applications of FDs, forcing and verifying FDs in PostgreSQL

Approximate Functional Dependencies (AFD):
introducing approximation in FDs as confidence measure. Clinical knowledge extraction using AFD: examples. AFD analysis in the biomedical context.

Algorithms for extracting AFDs:
minimal AFDs: definition, semantics and analysis. Theoretical Lower Bounds on the number of minimal AFD: the curse of cardinality. Basic algorthm for extracting minimal AFD. Compact representations of
sets of extracte AFDs. Randomized algorithms for extracting minimal AFDs:
theory and implementation.

Approximation in presence of measures:
Delta Functional Dependencies (DFDs) : definition, application, and verification. Analysis of DFDs extracted from the biomedical domain. Approximated DFDs
(ADFD):
definition, applications and analysis in the biomedical domain (examples). Algorithm for verifying single ADFD restricted to the case of 2 measures (2ADFD):
complexity, implementation. Extraction of minimal 2ADFD from clinical data.

Association Rules (ARs):
definition, examples in the biomedical domain. Extraction of di AR: support and confidence. Theoretical analysis: the curse of cardinality. Frequent Itemsets (FIs): definition, role in the extraction
of ARs, and algorithm for vandidates generation. ARs extraction from sets of FIs. Sets of FIs: minimal sets, closed sets.
Strategies for exploring FIs lattices. Alternatives to standard extraction algorithm using specific data structures (hash trees, FP-trees). Evaluation of association patterns: drawbacks of the support/confidence framework. Examples of paradoxes. alternative measures for association pattern analysis:
definition and examples.

Extraction Transformation and Loading (ETL):
definition, functions, role inside a data warehouse, data flows. Basic entities of ETL procedures and how they work: Job, Transformations, Job, Step, Transformation Step. Conceptual modelling of ETL procedures in Business Process Model and Notation (BPMN). Modelling examples: case studies. Embedding external procedures into ETL procedures: comunication, staging and managing of errors. API (Application Programming Interface) usage inside ETL procedures. Short description of XPATH constructs and how to use them. Screen scraping of websites in ETL procedures by using XPATH. Using Business Intellingence tools to realize ETL procedures.

Entropy-based classifiers:
introduction to the concept of Entropy. Decision Trees in the biomedical context. The Iterative Dichotomiser 3 (ID3) classifier: algorithm, examples and implementation. Measures discretization. Using ID3 for discretizing measures:
problems, modification and implementation. Temporal analysis application to adverse drug reactions.

Reporting and OLAP (Online Analytical Processing):
Interactive reporting systems: querying the clinical databases, parametrization of the reports. Dynamic retrieval of report information by using ETAL transformation. Modelling analysis using OLAP cubes and theri implementation: case studies. Using Business Intellingence tools to realize dynamic/interactive reports and OLAP cubes

SUGGESTED TEXTS:

DJ Hand, H Mannila, P Smyth
Principles of data mining
MIT Press Cambridge, MA, USA ©2001
ISBN:0-262-08290-X 9780262082907

Roland Bouman, Jos van Dongen
Pentaho Solutions: Business Intelligence and Data Warehousing with Pentaho and MySQL
Wiley Publishing, Inc.
ISBN: 978-0-470-48432-6
648 pages
September 2009

Fulton, Hal and Olsen, Russ
The ruby way: solutions and techniques in ruby programming, third edition
Addison-Wesley Professional ©2014
ISBN:0-321-71463-6

COURSE MATERIAL:

class slides;
example data (in .csv format) for completing the exercises proposed during classes;
implementation of the procedures introduced during the course.

Reference texts
Author Title Publishing house Year ISBN Notes
Roland Bouman, Jos van Dongen Pentaho Solutions: Business Intelligence and Data Warehousing with Pentaho and MySQL Wiley Publishing, Inc. 2009 978-0-470-48432-6
DJ Hand, H Mannila, P Smyth Principles of data mining MIT Press Cambridge 2001 9780262082907
Fulton, Hal and Olsen, Russ The ruby way: solutions and techniques in ruby programming (Edizione 3) Addison-Wesley Professional 2014 0-321-71463-6

Examination Methods

The exam modality aims to verify the autonomy and the skills of the student
in applying the concepts provided during the course for realising Decision Support Systems.
The exam consists of an interview on the implementation
of two projects assigned during classes, one for each macro-topic of the course:
1) Data Mining;
2) OLAP Analysis.
The two projects must be realised individually. Moreover, a necessary but not sufficient condition
for passing the exam is that both the implementations of the projects must be complete. In particular, each project will be evaluated on a scale going from 1 to 15 included, the final grade is given by the sum of the two individual
project grades.

There is no difference in the exam modality between students that attended the course and students that did not.

Bibliography

Type D and Type F activities

Training offer to be defined

Career prospects


Module/Programme news

News for students

There you will find information, resources and services useful during your time at the University (Student’s exam record, your study plan on ESSE3, Distance Learning courses, university email account, office forms, administrative procedures, etc.). You can log into MyUnivr with your GIA login details.

Further services

I servizi e le attività di orientamento sono pensati per fornire alle future matricole gli strumenti e le informazioni che consentano loro di compiere una scelta consapevole del corso di studi universitario.


Graduation


Attendance

As stated in point 25 of the Teaching Regulations for the A.Y. 2021/2022, attendance at the course of study is not mandatory.
Please refer to the Crisis Unit's latest updates for the mode of teaching.

Career management


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